Spectrum Sensing-Based Cognitive Radio Networks Model: A Hybrid Deep Learning-based Approach
| International Journal of Electrical and Electronics Engineering |
| © 2026 by SSRG - IJEEE Journal |
| Volume 13 Issue 2 |
| Year of Publication : 2026 |
| Authors : Nagla Elhaj Babiker, Khalid Hamid Bilal, Magdi B. M. Amien |
10.14445/23488379/IJEEE-V13I2P106
How to Cite?
Nagla Elhaj Babiker, Khalid Hamid Bilal, Magdi B. M. Amien, "Spectrum Sensing-Based Cognitive Radio Networks Model: A Hybrid Deep Learning-based Approach," SSRG International Journal of Electrical and Electronics Engineering, vol. 13, no. 2, pp. 83-98, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I2P106
Abstract:
In cognitive radio, optimum spectrum usage by secondary users depends on the detection of main user signals. There are several civilian and military uses of the ability to categorize incoming wireless signals. Recent impressive accomplishments in the field of wireless research have piqued the interest of researchers in using Deep Learning (DL), which is a subfield of Artificial Intelligence (AI), to solve the problem of modulation categorization. Recently, researchers released an updated version of the dataset, RadioML2016.10a, based on GNU Radio, which replicates the faults of a real wireless channel. This study proposed a Hybrid CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) model with a user-defined activation function (CLDNN-Act) for spectral sensing. This study also examines three alternative models to evaluate the effectiveness of the proposed paradigm. The evaluation parameters include recall, F1-score, precision, Bit Error Rate (BER), and signal probability detection.
Keywords:
Activation function, Cognitive radio, Convolutional Neural Network, Deep Learning, Long Short-Term Memory.
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